


"""simple docstring"""



import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def      UpperCAmelCase     (       _lowercase :   int	)						-> Optional[Any]:


	"""simple docstring"""




	return EnvironmentCommand()







class    __a						( _UpperCAmelCase							):







				@staticmethod
				def      lowerCamelCase_     ( UpperCAmelCase   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			parser.add_parser('''env'''   )
					download_parser.set_defaults(func=__UpperCamelCase   )







				def      lowerCamelCase_     ( self   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			huggingface_hub.__version__

					lowerCAmelCase_      							=			'''not installed'''
					lowerCAmelCase_      							=			'''NA'''
					if is_torch_available():
						import torch

						lowerCAmelCase_      							=			torch.__version__
						lowerCAmelCase_      							=			torch.cuda.is_available()

					lowerCAmelCase_      							=			'''not installed'''
					if is_transformers_available():
						import transformers

						lowerCAmelCase_      							=			transformers.__version__

					lowerCAmelCase_      							=			'''not installed'''
					if is_accelerate_available():
						import accelerate

						lowerCAmelCase_      							=			accelerate.__version__

					lowerCAmelCase_      							=			'''not installed'''
					if is_xformers_available():
						import xformers

						lowerCAmelCase_      							=			xformers.__version__

					lowerCAmelCase_      							=			{
					    '''`diffusers` version''': version,
					    '''Platform''': platform.platform(),
					    '''Python version''': platform.python_version(),
					    '''PyTorch version (GPU?)''': F"""{pt_version} ({pt_cuda_available})""",
					    '''Huggingface_hub version''': hub_version,
					    '''Transformers version''': transformers_version,
					    '''Accelerate version''': accelerate_version,
					    '''xFormers version''': xformers_version,
					    '''Using GPU in script?''': '''<fill in>''',
					    '''Using distributed or parallel set-up in script?''': '''<fill in>''',
					}

					print('''\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'''   )
					print(self.format_dict(__UpperCamelCase   )   )

					return info







				@staticmethod
				def      lowerCamelCase_     ( UpperCAmelCase   ):

					'''simple docstring'''






					return "\n".join([F"""- {prop}: {val}""" for prop, val in d.items()]   ) + "\n"

"""simple docstring"""



import argparse
from collections import OrderedDict
from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F

from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging


logging.set_verbosity_info()
a       :						Dict       		=		logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
a       :						Dict       		=		[]
for i in range(6):
 # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
 rename_keys.append(
     (F"transformer.encoder.layers.{i}.self_attn.out_proj.weight", F"encoder.layers.{i}.self_attn.out_proj.weight")
 )
 rename_keys.append(
     (F"transformer.encoder.layers.{i}.self_attn.out_proj.bias", F"encoder.layers.{i}.self_attn.out_proj.bias")
 )
 rename_keys.append((F"transformer.encoder.layers.{i}.linear1.weight", F"encoder.layers.{i}.fc1.weight"))
 rename_keys.append((F"transformer.encoder.layers.{i}.linear1.bias", F"encoder.layers.{i}.fc1.bias"))
 rename_keys.append((F"transformer.encoder.layers.{i}.linear2.weight", F"encoder.layers.{i}.fc2.weight"))
 rename_keys.append((F"transformer.encoder.layers.{i}.linear2.bias", F"encoder.layers.{i}.fc2.bias"))
 rename_keys.append(
     (F"transformer.encoder.layers.{i}.norm1.weight", F"encoder.layers.{i}.self_attn_layer_norm.weight")
 )
 rename_keys.append((F"transformer.encoder.layers.{i}.norm1.bias", F"encoder.layers.{i}.self_attn_layer_norm.bias"))
 rename_keys.append((F"transformer.encoder.layers.{i}.norm2.weight", F"encoder.layers.{i}.final_layer_norm.weight"))
 rename_keys.append((F"transformer.encoder.layers.{i}.norm2.bias", F"encoder.layers.{i}.final_layer_norm.bias"))
 # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.self_attn.out_proj.weight", F"decoder.layers.{i}.self_attn.out_proj.weight")
 )
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.self_attn.out_proj.bias", F"decoder.layers.{i}.self_attn.out_proj.bias")
 )
 rename_keys.append(
     (
         F"transformer.decoder.layers.{i}.multihead_attn.out_proj.weight",
         F"decoder.layers.{i}.encoder_attn.out_proj.weight",
     )
 )
 rename_keys.append(
     (
         F"transformer.decoder.layers.{i}.multihead_attn.out_proj.bias",
         F"decoder.layers.{i}.encoder_attn.out_proj.bias",
     )
 )
 rename_keys.append((F"transformer.decoder.layers.{i}.linear1.weight", F"decoder.layers.{i}.fc1.weight"))
 rename_keys.append((F"transformer.decoder.layers.{i}.linear1.bias", F"decoder.layers.{i}.fc1.bias"))
 rename_keys.append((F"transformer.decoder.layers.{i}.linear2.weight", F"decoder.layers.{i}.fc2.weight"))
 rename_keys.append((F"transformer.decoder.layers.{i}.linear2.bias", F"decoder.layers.{i}.fc2.bias"))
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.norm1.weight", F"decoder.layers.{i}.self_attn_layer_norm.weight")
 )
 rename_keys.append((F"transformer.decoder.layers.{i}.norm1.bias", F"decoder.layers.{i}.self_attn_layer_norm.bias"))
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.norm2.weight", F"decoder.layers.{i}.encoder_attn_layer_norm.weight")
 )
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.norm2.bias", F"decoder.layers.{i}.encoder_attn_layer_norm.bias")
 )
 rename_keys.append((F"transformer.decoder.layers.{i}.norm3.weight", F"decoder.layers.{i}.final_layer_norm.weight"))
 rename_keys.append((F"transformer.decoder.layers.{i}.norm3.bias", F"decoder.layers.{i}.final_layer_norm.bias"))

# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ('''input_proj.weight''', '''input_projection.weight'''),
        ('''input_proj.bias''', '''input_projection.bias'''),
        ('''query_embed.weight''', '''query_position_embeddings.weight'''),
        ('''transformer.encoder.norm.weight''', '''encoder.layernorm.weight'''),
        ('''transformer.encoder.norm.bias''', '''encoder.layernorm.bias'''),
        ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
        ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
        ('''class_embed.weight''', '''class_labels_classifier.weight'''),
        ('''class_embed.bias''', '''class_labels_classifier.bias'''),
        ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
        ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
        ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
        ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
        ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
        ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
    ]
)


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A   )     ->	Tuple:





    """simple docstring"""

    _UpperCAmelCase        = state_dict.pop(_A   )
    _UpperCAmelCase        = val


def 			_UpperCamelCase						(    _A   )     ->	str:





    """simple docstring"""

    _UpperCAmelCase        = OrderedDict()
    for key, value in state_dict.items():
        if "backbone.0.body" in key:
            _UpperCAmelCase        = key.replace("""backbone.0.body"""       ,   """backbone.conv_encoder.model"""   )
            _UpperCAmelCase        = value
        else:
            _UpperCAmelCase        = value

    return new_state_dict


def 			_UpperCamelCase						(    _A   )     ->	int:





    """simple docstring"""

    _UpperCAmelCase        = """"""

    # first: transformer encoder
    for i in range(6   ):
        # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
        _UpperCAmelCase        = state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""   )
        _UpperCAmelCase        = state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""   )
        # next, add query, keys and values (in that order) to the state dict
        _UpperCAmelCase        = in_proj_weight[:2_5_6, :]
        _UpperCAmelCase        = in_proj_bias[:2_5_6]
        _UpperCAmelCase        = in_proj_weight[2_5_6:5_1_2, :]
        _UpperCAmelCase        = in_proj_bias[2_5_6:5_1_2]
        _UpperCAmelCase        = in_proj_weight[-2_5_6:, :]
        _UpperCAmelCase        = in_proj_bias[-2_5_6:]
    # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
    for i in range(6   ):
        # read in weights + bias of input projection layer of self-attention
        _UpperCAmelCase        = state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""   )
        _UpperCAmelCase        = state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""   )
        # next, add query, keys and values (in that order) to the state dict
        _UpperCAmelCase        = in_proj_weight[:2_5_6, :]
        _UpperCAmelCase        = in_proj_bias[:2_5_6]
        _UpperCAmelCase        = in_proj_weight[2_5_6:5_1_2, :]
        _UpperCAmelCase        = in_proj_bias[2_5_6:5_1_2]
        _UpperCAmelCase        = in_proj_weight[-2_5_6:, :]
        _UpperCAmelCase        = in_proj_bias[-2_5_6:]
        # read in weights + bias of input projection layer of cross-attention
        _UpperCAmelCase        = state_dict.pop(
            F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""   )
        _UpperCAmelCase        = state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""   )
        # next, add query, keys and values (in that order) of cross-attention to the state dict
        _UpperCAmelCase        = in_proj_weight_cross_attn[:2_5_6, :]
        _UpperCAmelCase        = in_proj_bias_cross_attn[:2_5_6]
        _UpperCAmelCase        = in_proj_weight_cross_attn[2_5_6:5_1_2, :]
        _UpperCAmelCase        = in_proj_bias_cross_attn[2_5_6:5_1_2]
        _UpperCAmelCase        = in_proj_weight_cross_attn[-2_5_6:, :]
        _UpperCAmelCase        = in_proj_bias_cross_attn[-2_5_6:]


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	Optional[int]:





    """simple docstring"""

    _UpperCAmelCase     ,_UpperCAmelCase        = image.size
    _UpperCAmelCase        = max(_A       ,   _A   )
    _UpperCAmelCase        = 8_0_0 if """detection""" in checkpoint_url else 1_0_0_0
    _UpperCAmelCase        = target_max_size / current_max_size
    _UpperCAmelCase        = image.resize((int(round(scale * width   )   ), int(round(scale * height   )   ))   )

    return resized_image


def 			_UpperCamelCase						(    _A   )     ->	str:





    """simple docstring"""

    _UpperCAmelCase        = F.to_tensor(_A   )
    _UpperCAmelCase        = F.normalize(_A       ,   mean=[0.485, 0.456, 0.406]       ,   std=[0.229, 0.224, 0.225]   )
    return image


@torch.no_grad()
def 			_UpperCamelCase						(    _A       ,   _A       ,   _A   )     ->	str:





    """simple docstring"""

    logger.info("""Converting model..."""   )

    # load original state dict
    _UpperCAmelCase        = torch.hub.load_state_dict_from_url(_A       ,   map_location="""cpu"""   )
    # rename keys
    for src, dest in rename_keys:
        rename_key(_A       ,   _A       ,   _A   )
    _UpperCAmelCase        = rename_backbone_keys(_A   )
    # query, key and value matrices need special treatment
    read_in_q_k_v(_A   )
    # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
    _UpperCAmelCase        = """model."""
    for key in state_dict.copy().keys():
        if not key.startswith("""class_labels_classifier"""   ) and not key.startswith("""bbox_predictor"""   ):
            _UpperCAmelCase        = state_dict.pop(_A   )
            _UpperCAmelCase        = val
    # create HuggingFace model and load state dict
    _UpperCAmelCase        = TableTransformerConfig(
        backbone="""resnet18"""       ,   mask_loss_coefficient=1       ,   dice_loss_coefficient=1       ,   ce_loss_coefficient=1       ,   bbox_loss_coefficient=5       ,   giou_loss_coefficient=2       ,   eos_coefficient=0.4       ,   class_cost=1       ,   bbox_cost=5       ,   giou_cost=2       ,   )

    if "detection" in checkpoint_url:
        _UpperCAmelCase        = 1_5
        _UpperCAmelCase        = 2
        _UpperCAmelCase        = {0: """table""", 1: """table rotated"""}
        _UpperCAmelCase        = idalabel
        _UpperCAmelCase        = {v: k for k, v in idalabel.items()}
    else:
        _UpperCAmelCase        = 1_2_5
        _UpperCAmelCase        = 6
        _UpperCAmelCase        = {
            0: """table""",
            1: """table column""",
            2: """table row""",
            3: """table column header""",
            4: """table projected row header""",
            5: """table spanning cell""",
        }
        _UpperCAmelCase        = idalabel
        _UpperCAmelCase        = {v: k for k, v in idalabel.items()}

    _UpperCAmelCase        = DetrImageProcessor(
        format="""coco_detection"""       ,   max_size=8_0_0 if """detection""" in checkpoint_url else 1_0_0_0   )
    _UpperCAmelCase        = TableTransformerForObjectDetection(_A   )
    model.load_state_dict(_A   )
    model.eval()

    # verify our conversion
    _UpperCAmelCase        = """example_pdf.png""" if """detection""" in checkpoint_url else """example_table.png"""
    _UpperCAmelCase        = hf_hub_download(repo_id="""nielsr/example-pdf"""       ,   repo_type="""dataset"""       ,   filename=_A   )
    _UpperCAmelCase        = Image.open(_A   ).convert("""RGB"""   )
    _UpperCAmelCase        = normalize(resize(_A       ,   _A   )   ).unsqueeze(0   )

    _UpperCAmelCase        = model(_A   )

    if "detection" in checkpoint_url:
        _UpperCAmelCase        = (1, 1_5, 3)
        _UpperCAmelCase        = torch.tensor(
            [[-6.7_897, -16.9_985, 6.7_937], [-8.0_186, -22.2_192, 6.9_677], [-7.3_117, -21.0_708, 7.4_055]]   )
        _UpperCAmelCase        = torch.tensor([[0.4_867, 0.1_767, 0.6_732], [0.6_718, 0.4_479, 0.3_830], [0.4_716, 0.1_760, 0.6_364]]   )

    else:
        _UpperCAmelCase        = (1, 1_2_5, 7)
        _UpperCAmelCase        = torch.tensor(
            [[-18.1_430, -8.3_214, 4.8_274], [-18.4_685, -7.1_361, -4.2_667], [-26.3_693, -9.3_429, -4.9_962]]   )
        _UpperCAmelCase        = torch.tensor([[0.4_983, 0.5_595, 0.9_440], [0.4_916, 0.6_315, 0.5_954], [0.6_108, 0.8_637, 0.1_135]]   )

    assert outputs.logits.shape == expected_shape
    assert torch.allclose(outputs.logits[0, :3, :3]       ,   _A       ,   atol=1e-4   )
    assert torch.allclose(outputs.pred_boxes[0, :3, :3]       ,   _A       ,   atol=1e-4   )
    print("""Looks ok!"""   )

    if pytorch_dump_folder_path is not None:
        # Save model and image processor
        logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""   )
        Path(_A   ).mkdir(exist_ok=_A   )
        model.save_pretrained(_A   )
        image_processor.save_pretrained(_A   )

    if push_to_hub:
        # Push model to HF hub
        logger.info("""Pushing model to the hub..."""   )
        _UpperCAmelCase        = (
            """microsoft/table-transformer-detection"""
            if """detection""" in checkpoint_url
            else """microsoft/table-transformer-structure-recognition"""
        )
        model.push_to_hub(_A   )
        image_processor.push_to_hub(_A   )


if __name__ == "__main__":
 a       :						Dict       		=		argparse.ArgumentParser()

 parser.add_argument(
     '''--checkpoint_url''',
     default='''https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth''',
     type=str,
     choices=[
         '''https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth''',
         '''https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth''',
     ],
     help='''URL of the Table Transformer checkpoint you\'d like to convert.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
 )
 parser.add_argument(
     '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
 )
 a       :						int       		=		parser.parse_args()
 convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)